System for local optimization of object detector based on deep neural network and method of creating local database therefor

ABSTRACT

Provided is a method of creating a local database for local optimization of an object detector based on a deep neural network. The method includes performing preprocessing on an image extracted from real-time collected or pre-collected images from an edge device, modeling a static background image based on the image received through the pre-processing unit and calculating a difference image between a current input image and a background model to model a dynamic foreground image, detecting an object image from the image based on a training model, and creating a local database based on the background image, the foreground image synthesized with the background image, and the object image synthesized with the background image.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to and the benefit of Korean PatentApplication No. 10-2020-0147645, filed on Nov. 6, 2020, the disclosureof which is incorporated herein by reference in its entirety.

BACKGROUND 1. Field of the Invention

The present invention relates to a system for local optimization of anobject detector based on a deep neural network and a method of creatinga local database therefor.

2. Discussion of Related Art

Recently, edge-computing technologies, which process a vast amount ofdata in real time through distributed small servers, that is, edgedevices, not through a centralized server, are being actively researchedand developed. As Internet of Things (IoT) devices spread in earnest,the amount of data has skyrocketed, and thus, cloud computing hasreached its limit. To cope with the above problem, edge-computingtechnologies have been developed.

In the edge-computing technologies, since low-cost edge devices aremainly used and resources such as computational performance and memoryare limited to minimize heat generation and power, a lightweight objectdetection algorithm is used for real-time processing.

The lightweight object detection algorithm does not provide a high levelof accuracy in all environments, and therefore, re-learned data iscollected in the installation environment of the edge devices to performlocal optimization.

However, learning data needs to be reconstructed using the collecteddata according to the purpose of the algorithm and the localenvironment, which entails additional manpower and time costs.

RELATED ART DOCUMENT Patent Document

Korean Patent Laid-Open Publication No. 10-2016-0071781 (Jun. 22, 2016)

SUMMARY OF THE INVENTION

The present invention is directed to a system for local optimization ofan object detector based on a deep neural network that constructs dataof a corresponding area from a fixed surveillance image using apre-trained model and a background model and optimizes an edge terminalfor the corresponding area through a re-learning and tuning processbased on the constructed optimization database, and a method of creatinglocal database therefor.

However, the problems to be solved by the present invention are notlimited to the above problems, and other problems may exist.

According to an aspect of the present invention, there is provided asystem for local optimization of an object detector based on a deepneural network, the system including: a server configured to create atrained training model based on a public database including data and acorrect answer collected online and offline, download a local databaseand process the downloaded local database together with the publicdatabase to create an optimization database, and create an optimizationmodel through a learning process based on the optimization database; andat least one edge device configured to extract an image from real-timecollected or pre-collected images, receive the training model uploadedfrom the server, and extract an object image from the image based on thetraining model, input the extracted image into a background model tocreate a background image and a foreground image, create the localdatabase based on the object image, the background image, and theforeground image and transmit the created local database to the server,and receive the optimization model from the server to create an objectimage detection result from the image.

According to another aspect of the present invention, there is provideda system for local optimization of an object detector based on a deepneural network, the system including: a server configured to receive animage extracted from real-time collected or pre-collected images, createa trained training model based on a public database including data andcorrect answers collected online and offline and extract an object imagefrom the image based on the training model, input the image to apre-stored background model to create a background image and aforeground image, create the local database based on the object image,the background image, and the foreground image, process the localdatabase and the public database together to create an optimizationdatabase, and create an optimization model through a learning processbased on the optimization database; and at least one edge deviceconfigured to collect the image and transmit the collected images to theserver and receive the optimization model from the server to create anobject image detection result from the image.

According to still another aspect of the present invention, there isprovided a method of creating a local database for local optimization ofan object detector based on a deep neural network, the method including:performing preprocessing on an image extracted from real-time collectedor pre-collected images from an edge device; modeling a staticbackground image based on the image received through the pre-processingunit and calculating a difference image between a current input imageand a background model to model a dynamic foreground image; detecting anobject image from the image based on a training model; and creating alocal database based on the background image, the foreground imagesynthesized with the background image, and the object image synthesizedwith the background image.

According to another aspect of the present invention for solving theabove-described problems, a computer program is combined with acomputer, which is hardware, to execute the method of creating a localdatabase for local optimization of an object detector based on a deepneural network and is stored in a computer-readable recording medium.

Other specific details of the present invention are included in thedetailed description and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other objects, features and advantages of the presentinvention will become more apparent to those of ordinary skill in theart by describing exemplary embodiments thereof in detail with referenceto the accompanying drawings, in which:

FIG. 1 is a block diagram of a system for local optimization of anobject detector according to an embodiment of the present invention;

FIG. 2 is a block diagram of a system for local optimization of anobject detector according to another embodiment of the presentinvention;

FIG. 3 is a diagram for describing a process of creating a localdatabase;

FIG. 4A to 4C are diagram for describing an image of a processing resultby a preprocessing unit;

FIG. 5A to 5D are view for describing an image of a processing result bya background modeling unit;

FIG. 6A to 6D are diagram for describing an image of a processing resultby a post-processing unit; and

FIG. 7 is a flowchart of a method of creating a local database.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

Various advantages and features of the present invention and methodsaccomplishing them will become apparent from the following descriptionof embodiments with reference to the accompanying drawings. However, thepresent invention is not limited to exemplary embodiments to bedescribed below but may be implemented in various different forms, andthese exemplary embodiments will be provided only in order to make thepresent invention complete and allow those skilled in the art tocompletely recognize the scope of the present invention, and the presentinvention will be defined by the scope of the claims.

Terms used in the present specification are for explaining embodimentsrather than limiting the present invention. Unless otherwise stated, asingular form includes a plural form in the present specification.Throughout this specification, the terms “comprise” and/or “comprising”will be understood to imply the inclusion of stated constituents but notthe exclusion of any other constituents. Like reference numerals referto like components throughout the specification and “and/or” includeseach of the components described and includes all combinations thereof.Although “first,” “second” and the like are used to describe variouscomponents, it goes without saying that these components are not limitedby these terms. These terms are used only to distinguish one componentfrom other components. Therefore, it goes without saying that the firstcomponent described below may be the second component within thetechnical scope of the present invention.

Unless defined otherwise, all terms (including technical and scientificterms) used in the present specification have the same meaning asmeanings commonly understood by those skilled in the art to which thepresent invention pertains. In addition, terms defined in commonly useddictionaries are not to be ideally or excessively interpreted unlessexplicitly defined otherwise.

Hereinafter, a system 1 for local optimization of an object detectorbased on a deep neural network (hereinafter, system for localoptimization of an object detector) according to an embodiment of thepresent invention will be described with reference to FIGS. 1 to 6.

FIG. 1 is a block diagram of a system 1 for local optimization of anobject detector according to an embodiment of the present invention.

The system 1 for local optimization of an object detector according tothe embodiment of the present invention includes a server 100 and atleast one edge device 200. In this case, FIG. 1 illustrates an examplein an offline state in which an image collected by the edge device 200may not be transmitted to the server 100.

The server 100 includes a public database and an optimization databaseas a database and includes a training model and an optimization model.

Specifically, the server 100 creates a trained training model based onan open public database including data and correct answers collectedonline and offline. The training model created in this way is uploadedto the edge device 200, and the edge device 200 detects an object basedon the training model to create a local database.

In addition, the server 100 downloads the local database created fromthe edge device 200 and processes the downloaded local database togetherwith the public database to create an optimization database, creates anoptimization model through a re-learning and tuning process based on theoptimization database and then provides the optimization model to theedge device 200.

The edge device 200 extracts an image from images collected in areal-time or pre-recorded form. In this case, the edge device 200 maycollect a fixed surveillance image. The edge device 200 receives atraining model learned from the initial server 100 by targeting thecollected images and extracts an object image to be used for localoptimization.

In addition, the edge device 200 creates a static background image byinputting the extracted image to a background model and creates adynamic foreground image by calculating a difference image between thecurrent input image and the background model.

Then, the edge device 200 synthesizes each of the foregrounds of theforeground image and the object image extracted from the training modelwith the background image based on reliability and creates a localdatabase based on the background image, the foreground image synthesizedwith the background image, and the object image synthesized with thebackground image.

The server 100 downloads the local database created in this way andcreates an optimization model as described above and uploads the createdoptimization model to the edge device 200, and the edge device 200detects an object image result from the image according to the uploadedoptimization model.

FIG. 2 is a block diagram of a system 1 for local optimization of anobject detector according to another embodiment of the presentinvention.

The system 1 for local optimization of an object detector according tothe embodiment of the present invention includes a server 100 and atleast one edge device 200. In this case, unlike FIG. 1, the embodimentof FIG. 2 illustrates an example in an online state in which an imagecollected from the edge device 200 can be transmitted to the server 100.

First, the edge device 200 extracts an image from real-time collected orpre-collected fixed surveillance images and transmits the extractedimage to the server 100.

The server 100 creates a trained training model based on a publicdatabase including data and correct answers collected online and offlineand then extracts an object image from the image received from the edgedevice 200 based on the training model.

In addition, the server 100 inputs an image into a pre-stored backgroundmodel to create a background image and a foreground image. In this case,the server 100 calculates a difference image between the current inputimage and the background model to create the foreground image. Theserver 100 creates a local database based on the background image, theforeground image synthesized with the background image, and the objectimage synthesized with the background image.

Then, the server 100 processes the local database and the publicdatabase together to generate an optimization database and creates anoptimization model through a learning process based on the optimizationdatabase.

Thereafter, the edge device 200 downloads the optimization model fromthe server 100 and creates an object image detection result from theimage.

In the system 1 for local optimization of an object detector accordingto the embodiment of the present invention described with reference toFIGS. 1 and 2, when the server 100 and the edge device 200 are in anoffline state, the edge device 200 directly creates the local databaseand uploads the created local database to the server 100, and theoptimized model in the server 100 is downloaded to the edge device 200after a training process. On the other hand, when the server 100 and theedge device 200 are in an online state, the edge device 200 uploads animage to the server 100, performs the optimization in the server 100,and then transmits the optimized model to the edge device 200.

Hereinafter, a process of creating a local database for optimizationcommonly performed in FIGS. 1 and 2 will be described with reference toFIG. 3.

FIG. 3 is a diagram for describing a process of creating a localdatabase. FIG. 4A to 4C are diagram for describing an image of aprocessing result by a pre-processing unit. FIG. 5A to 5D are view fordescribing an image of a processing result by a background modeling unit320. FIG. 6A to 6D are diagram for describing an image of a processingresult by a post-processing unit 340.

In this case, in the case of the embodiment of FIG. 1, the edge device200 creates the local database, and in the case of the embodiment ofFIG. 2, the server 100 creates the local database. Hereinafter, in thedescription of FIG. 3, the creation of the local database in the edgedevice 200 will be mainly described for convenience.

The edge device 200 includes a preprocessing unit 310, a backgroundmodeling unit 320, a detection unit 330, and a post-processing unit 340.

Specifically, the preprocessing unit 310 extracts an image fromreal-time images or stored images as illustrated in FIG. 4A and receivesthe extracted image. Then, an RGB color space of the image is convertedinto a predetermined type of color space to correspond tocharacteristics of the image. For example, the preprocessing unit 310may convert the red/green/blue (RGB) color space into Gray, HSB, LAB,YCrCb, or the like according to the characteristics of the image.

In addition, the preprocessing unit 310 may create an enhanced image byapplying a predetermined filter to the converted image. In this case, asthe predetermined filter, various filters for edge enhancement, noiseremoval, and the like may be applied.

Then, the preprocessing unit 310 splits a channel to correspond to thecolor space of the converted image. In an embodiment, the preprocessingunit 310 splits or extracts a required channel from a multi-channelimage. In particular, in the case of a single channel, the preprocessingunit 310 splits the single channel into the multi-channel by applyinginversion or the like. For example, a hue/saturation/value (HSV) imagemay be split into each channel of color, saturation, and brightness, andin the case of black and white, bright objects and dark objects may bedetected without omission through inversion.

Since a black (pixel value is 0) object is lost and is impossible todetect when calculating a difference image later, the bright objects maybe enhanced with the preprocessed black and white image as illustratedin FIG. 4B, and the dark objects may be enhanced with preprocessedinverted images as illustrated in FIG. 4C.

The background modeling unit 320 models a static background image basedon the image received through the preprocessing unit 310 and calculatesa difference image between the current input image and the backgroundmodel to model a dynamic foreground image.

Specifically, the background modeling unit 320 may include a pluralityof static models and dynamic models. The plurality of static modelsreceive an enhanced image or a channel-split image from thepreprocessing unit 310 and model the static background image based on n(n is a natural number greater than or equal to two) consecutive pastimages from a current image t as illustrated in FIGS. 5A and 5B. In thiscase, n is the number of past frames required for a static model, and nmay be adjusted based on the amount of change in motion of a dynamicobject.

A plurality of dynamic models are extracted using the difference imagebetween the current input image and the background model as illustratedin FIGS. 5C and 5D, and the dynamic model may compensate for the loss ofthe dark objects using multi-channel images created by the preprocessingunit 310.

That is, FIG. 5 illustrates the static model after the pre-processing.When only the pre-processed black and white image according to FIG. 5Ais used, it may be confirmed through FIG. 5C that all dark vehiclesillustrated in FIG. 4B are lost. In order to compensate for thisproblem, on the contrary, as illustrated in FIG. 4C, the dark vehiclepart may be brightened by using the inverted image to brighten theoriginally dark vehicle as illustrated in FIG. 5D. However, in thiscase, since the brightened vehicle part is lost, according to anembodiment of the present invention, it is possible to detect allobjects without a lost part as illustrated in FIG. 6A by combining FIGS.5C and 5D.

Next, the detection unit 330 detects an object image from the imagebased on the training model. The detection unit 330 detects an objectimage from an input image using a training model learned using thepublic database described in FIGS. 1 and 2.

The detection unit arranges detected object images based on detectionreliability and then transmits the object image having detectionreliability greater than or equal to a threshold value to thepost-processing unit.

Next, the post-processing unit 340 merges the images received throughthe background modeling unit 320. In this case, the post-processing unit340 may merge images in various ways, such as batch merging, channelweight merging, and dynamic model mask.

In addition, the post-processing unit 340 performs erosion and dilationoperations, which are morphology operations, on the merged image. Thatis, the post-processing unit 340 may perform the morphology operation toremove noise generated from the difference image or merged image or tosupplement lost pixels. In this case, the post-processing unit 340 mayadjust the order of morphology operations and the number of morphologyoperations according to a ratio of noise and loss.

Next, the post-processing unit 340 performs a binarization process ofclassifying background and foreground on the result of performing themorphology operation, and may be binarized into a candidate group ofobjects of each pixel and other backgrounds and noises. In this case,various adaptive methods such as sampling, average, and median values ofreference pixels may be used as a division value for performing thebinarization process.

Thereafter, the post-processing unit 340 extracts (contours) contourinformation of the foreground object from a result of the binarizationprocess and approximates the extracted contour information to create(fit) box information corresponding to the object image which is used tocreate the optimization database. Here, a distant object may be excludedfrom the optimization process because a large number of candidate groupsmay be combined, or an object that is too small may be removed as noise,making it difficult to smoothly fit.

As the above process is completed, as described above, the edge device200 creates the local database based on the object image, the backgroundimage, and the foreground image and transmits the created local databaseto the server 100.

In this case, the background image and each synthesized image createdaccording to each process in an embodiment of the present inventionperform the following roles.

First, the background image is an image in which only a backgroundwithout an object to be detected exists. Learning the background imagemay serve to remove false detection rather than correct answers. Thatis, the non-optimized detector erroneously recognizes an object to bedetected, such as a traffic light or a building with a shadowbackground, and thus, the false detection occurs, which can be removedthrough the learning of the background image.

In addition, each synthesized image increases the learning data of theobjects to be detected appearing in the corresponding area, therebyimproving the reliability (0 to 100%) of the detected object andincreasing the discrimination. In addition, when each synthesized imageis used, there is an advantage in that non-detection can be reduced byadditionally learning objects that were not detected by the existingdetector as a foreground synthesized image. That is, since the objectimage and the synthesized image depend on a non-optimized detector andthe foreground image and the synthesized image use the image processingof the background modeling, each synthesized image complements eachother so as to reduce non-detected objects.

Hereinafter, a method of creating a local database for localoptimization of an object detector based on a deep neural network(hereinafter, method of creating local database) according to anembodiment of the present invention will be described with reference toFIG. 7.

FIG. 7 is a flowchart of a method of creating local database.

Meanwhile, operations illustrated in FIG. 7 may be understood to beperformed by the server 100 or the edge device 200, but the presentinvention is not limited thereto. Hereinafter, for convenience, thepresent invention will be described assuming that operations areperformed by the server 100.

First, the server 100 performs pre-processing on an image extracted fromreal-time collected images or pre-collected images from the edge device200 (S110).

Next, the server 100 models the static background image based on thepreprocessed image and calculates the difference image between a currentinput image and the background model to model the dynamic foregroundimage (S120).

Next, the server 100 detects an object image from the image based on thetraining model (S130) and creates a local database based on thebackground image, the foreground image synthesized with the backgroundimage, and the object image synthesized with the background image(S140).

Meanwhile, in the above description, operations S401 to S407 may befurther divided into additional operations or combined into feweroperations according to the implementation example of the presentinvention. In addition, some operations may be omitted if necessary, andthe order between the operations may be changed. In addition, even whenother content is omitted, the content of the system 1 for localoptimization of an object detector of FIGS. 1 to 6 may also be appliedto the content of FIG. 7.

The components of the present invention described above may be embodiedas a program (or application) and stored in a medium for execution incombination with a computer which is hardware.

In order for the computer to read the program and execute the methodsimplemented as a program, the program may include code coded in acomputer language such as C/C++, C#, JAVA, Python, machine language, andthe like that the processor (central processing unit (CPU)) of thecomputer can read through a device interface of the computer. Such codemay include functional code related to functions defining functionsnecessary for executing the methods, or the like, and include anexecution procedure related control code necessary for the processor ofthe computer to execute the functions according to a predeterminedprocedure. In addition, such code may further include a memory referencerelated code for which location (address, house number) of the internalor external memory of the computer additional information or medianecessary for the processor of the computer to execute the functionsshould be referenced. In addition, when the processor of the computerneeds to communicate with any other computers, servers, or the likelocated remotely in order to execute the above functions, the code mayfurther include a communication-related code for how to communicate withany other computers, servers, or the like located remotely using acommunication module of the computer, how to transmit/receive anyinformation or media during communication, or the like.

The storage medium is not a medium that stores data therein for a while,such as a register, a cache, a memory, or the like, but rather means amedium that semi-permanently stores data therein and is readable by adevice. Specifically, examples of the storage medium include, but arenot limited to, a read-only memory (ROM), a random-access memory (RAM),a compact disc (CD)-ROM, a magnetic tape, a floppy disk, an optical datastorage device, and the like. That is, the program may be stored invarious recording media on various servers accessible by the computer orin various recording media on the computer of the user. In addition, themedium may be distributed in a computer system connected through anetwork, and the medium may store computer-readable codes in adistributed manner.

The above description of the present invention is for illustrativepurposes, and those skilled in the art to which the present inventionpertains will understand that it is possible to be easily modified toother specific forms without changing the technical spirit or essentialfeatures of the present invention. Therefore, it should be understoodthat the above-described embodiments are exemplary in all aspects butare not limited thereto. For example, each component described as asingle type may be implemented in a distributed manner, and similarly,components described as distributed may be implemented in a combinedform.

It is to be understood that the scope of the present invention will bedefined by the claims rather than the above-described description andall modifications and alternations derived from the claims and theirequivalents are included in the scope of the present invention.

According to an embodiment of the present invention described above, itis possible to efficiently operate a lightweight algorithm that operatesat high performance in real time through local optimization of an edgeterminal.

In addition, since the local database used for optimization uses apre-trained model, a background image, and a foreground image together,it is possible to implement local optimization only with the backgroundimage, prevent annotation omissions by increasing data based on asynthesis of foreground and object with background, and improve accuracythereof.

The effects of the present invention are not limited to theabove-described effects, and other effects that are not described may beobviously understood by those skilled in the art from the above detaileddescription.

What is claimed is:
 1. A system for local optimization of an objectdetector based on a deep neural network, the system comprising: a serverconfigured to create a trained training model based on a public databaseincluding data and a correct answer collected online and offline,download a local database and process the downloaded local databasetogether with the public database to create an optimization database,and create an optimization model through a learning process based on theoptimization database; and at least one edge device configured toextract an image from real-time collected or pre-collected images,receive the training model uploaded from the server, and extract anobject image from the image based on the training model, input theextracted image into a background model to create a background image anda foreground image, create the local database based on the object image,the background image, and the foreground image, and transmit the createdlocal database to the server, and receive the optimization model fromthe server to create an object image detection result from the image. 2.The system of claim 1, wherein the edge device calculates a differenceimage between the background model and a current input image to createthe foreground image.
 3. The system of claim 1, wherein the edge devicecreates the local database based on the background image, the foregroundimage synthesized with the background image, and the object imagesynthesized with the background image.
 4. The system of claim 1, whereinthe edge device collects a fixed surveillance image as the image.
 5. Thesystem of claim 1, wherein the edge device includes: a preprocessingunit configured to convert a red/green/blue (RGB) color space of theimage into a predetermined type of color space to correspond tocharacteristics of the image, apply a predetermined filter to theconverted image to create an enhanced image, and split a channel tocorrespond to a color space of the converted color space; a backgroundmodeling unit configured to model a static background image based on theimage received through the preprocessing unit and calculate a differenceimage between a current input image and the background image to model adynamic foreground image; a detection unit configured to detect anobject image from the image based on the training model; and apost-processing unit configured to merge images received through thebackground modeling unit, perform erosion and dilation operations, whichare morphology operations, on the merged image, perform a binarizationprocess of splitting the background and the foreground based on a resultof performing the morphology operations and then extract contourinformation of a foreground object based on a result of the binarizationprocess, and approximate the extracted contour information to create boxinformation corresponding to the object image which is used to createthe optimization database.
 6. The system of claim 5, wherein thebackground modeling unit receives the enhanced image or thechannel-split image from the preprocessing unit and models the staticbackground image based on n consecutive past images (n is a naturalnumber greater than or equal to two) from a current image.
 7. The systemof claim 6, wherein the n past images are adjusted based on an amount ofchange in motion of a dynamic object.
 8. The system of claim 5, whereinthe detection unit arranges detected object images based on detectionreliability and then transmits the object image having detectionreliability greater than or equal to a threshold value to thepost-processing unit.
 9. The system of claim 5, wherein thepost-processing unit adjusts an order of the morphology operations andthe number of morphology operations based on a ratio of noise and lossof the merged image.
 10. The system of claim 5, wherein thepost-processing unit binarizes each pixel into a candidate group of anobject and other background and noise based on a result of performingthe morphology operation.
 11. A system for local optimization of anobject detector based on a deep neural network, the system comprising: aserver configured to receive an image extracted from real-time collectedor pre-collected images, create a trained training model based on apublic database including data and correct answers collected online andoffline and extract an object image from the image based on the trainingmodel, input the image to a pre-stored background model to create abackground image and a foreground image, create the local database basedon the object image, the background image, and the foreground image,process the local database and the public database together to create anoptimization database, and create an optimization model through alearning process based on the optimization database; and at least oneedge device configured to collect the image and transmit the collectedimage to the server and receive the optimization model from the serverto create an object image detection result from the image.
 12. Thesystem of claim 11, wherein the server calculates a difference imagebetween a current input image and the background image to create theforeground image.
 13. The system of claim 11, wherein the server createsthe local database based on the background image, the foreground imagesynthesized with the background image, and the object image synthesizedwith the background image.
 14. The system of claim 11, wherein the edgedevice collects a fixed surveillance image as the image.
 15. The systemof claim 11, wherein the server includes: a preprocessing unitconfigured to convert a red/green/blue (RGB) color space of the imageinto a predetermined type of color space to correspond tocharacteristics of the image, apply a predetermined filter to theconverted image to create an enhanced image, and split a channel tocorrespond to a color space of the converted color space; a backgroundmodeling unit configured to model a static background image based on theimage received through the preprocessing unit and calculate thedifference image between a current input image and the background imageto model a dynamic foreground image; a detection unit configured todetect an object image from the image based on the training model; and apost-processing unit configured to merge the images received through thebackground modeling unit, perform erosion and dilation operations, whichare morphology operations, on the merged image, perform a binarizationprocess of splitting the background and the foreground based on a resultof performing the morphology operation and then extract contourinformation of a foreground object based on a result of the binarizationprocess, and approximate the extracted contour information to create boxinformation corresponding to the object image which is used to createthe optimization database.
 16. The system of claim 15, wherein thebackground modeling unit receives the enhanced image or a channel-splitimage from the preprocessing unit and models the static background imagebased on n consecutive past images (n is a natural number greater thanor equal to two) from a current image.
 17. The system of claim 16,wherein the n past images are adjusted based on an amount of change inmotion of a dynamic object.
 18. The system of claim 15, wherein thedetection unit arranges detected object images based on detectionreliability and then transmits the object image having detectionreliability greater than or equal to a threshold value to thepost-processing unit.
 19. The system of claim 15, wherein thepost-processing unit adjusts an order of the morphology operations andthe number of morphology operations based on a ratio of noise and lossof the merged image.
 20. A method of creating a local database for localoptimization of an object detector based on a deep neural network, whichis executed by a computer, the method comprising: performingpreprocessing on an image extracted from real-time collected orpre-collected images from an edge device; modeling a static backgroundimage based on the image received through the pre-processing unit andcalculating a difference image between a current input image and abackground model to model a dynamic foreground image; detecting anobject image from the image based on a training model; and creating alocal database based on the background image, the foreground imagesynthesized with the background image, and the object image synthesizedwith the background image.